Determining end times for single page applications

ABSTRACT

In one embodiment, a monitoring process detects a page load start time of a single page application (SPA) page having added direct resources and dynamic resources, tracks the direct resources and dynamic resources, and notes a load end time for each of the tracked direct resources and dynamic resources. The monitoring process stops the tracking of the direct resources and dynamic resources in response to a determination of a threshold duration of network inactivity, and determines a maximum load end time of the tracked direct resources and dynamic resources. Accordingly, the monitoring process may then set a page load time of the SPA page as a difference between the maximum load end time and the page load start time.

TECHNICAL FIELD

The present disclosure relates generally to computer networks, and, moreparticularly, to determining end times for single page applications(SPAs).

BACKGROUND

The Internet and the World Wide Web have enabled the proliferation ofweb services available for virtually all types of businesses. Due to theaccompanying complexity of the infrastructure supporting the webservices, it is becoming increasingly difficult to maintain the highestlevel of service performance and user experience to keep up with theincrease in web services. For example, it can be challenging to piecetogether monitoring and logging data across disparate systems, tools,and layers in a network architecture. Moreover, even when data can beobtained, it is difficult to directly connect the chain of events andcause and effect.

In one particular example, a Single Page Application (SPA) is a webbrowser application that interacts with a user by dynamically rewritinga current web page rather than loading entire new pages from a server.In an SPA's first load, all necessary code to construct the webapplication is retrieved in an initial single page load, then additionalcode, data, and resources can be loaded by “XMLHttpRequest” requests(XHRs) (XML—Extensible Markup Language; HTTP—Hypertext TransferProtocol). After that, page transitions will simply be content changesthrough XHR requests or memory state changes. Because these are not fullpage loads, they are generally referred to as “virtual pages” or“virtual pageviews”.

Since Single Page Apps (SPAs) have changed the loading behavior of webpages, however, traditional performance metric monitoring techniques areinadequate. That is, conventional agents that are designed to monitorweb application performance data in browsers have not kept up with thenewer loading behaviors of SPAs, and thus continue to report inaccuratemetrics.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein may be better understood by referring to thefollowing description in conjunction with the accompanying drawings inwhich like reference numerals indicate identically or functionallysimilar elements, of which:

FIGS. 1A-1B illustrate an example computer network;

FIG. 2 illustrates an example computing device/node;

FIG. 3 illustrates an example application intelligence platform;

FIG. 4 illustrates an example system for an application-aware intrusiondetection system;

FIG. 5 illustrates an example computing system implementing thedisclosed technology;

FIG. 6 illustrates an example of dynamic resources loading prior tovirtual page transitions;

FIG. 7 illustrates an example of start times of virtual pagetransitions;

FIG. 8 illustrates an example of wrapping events with a causal eventsstack;

FIG. 9 illustrates an example of causality chaining;

FIGS. 10A-10D illustrate examples of root cause correlation betweenevents and virtual pages;

FIG. 11 illustrates an example of a waiting queue for propercorrelation;

FIG. 12 illustrates an example procedure for cause-based event (e.g.,XHR) correlation to virtual page transitions in single page applications(SPAs) in accordance with one or more embodiments described herein;

FIG. 13 illustrates an example of load time computation based on loadedresources;

FIG. 14 illustrates another example of load time computation based onloaded resources;

FIG. 15 illustrates an example of load listener load times forresources;

FIG. 16 illustrates an example of a determination of load/end times ofan SPA page; and

FIG. 17 illustrates an example procedure for determining load/end timesfor SPAs in accordance with one or more embodiments described herein.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

According to one or more embodiments of the disclosure, a monitoringprocess detects a page load start time of a single page application(SPA) page having added direct resources and dynamic resources, tracksthe direct resources and dynamic resources, and notes a load end timefor each of the tracked direct resources and dynamic resources. Themonitoring process stops the tracking of the direct resources anddynamic resources in response to a determination of a threshold durationof network inactivity, and determines a maximum load end time of thetracked direct resources and dynamic resources. Accordingly, themonitoring process may then set a page load time of the SPA page as adifference between the maximum load end time and the page load starttime.

Description

A computer network is a geographically distributed collection of nodesinterconnected by communication links and segments for transporting databetween end nodes, such as personal computers and workstations, or otherdevices, such as sensors, etc. Many types of networks are available,ranging from local area networks (LANs) to wide area networks (WANs).LANs typically connect the nodes over dedicated private communicationslinks located in the same general physical location, such as a buildingor campus. WANs, on the other hand, typically connect geographicallydispersed nodes over long-distance communications links, such as commoncarrier telephone lines, optical lightpaths, synchronous opticalnetworks (SONET), synchronous digital hierarchy (SDH) links, orPowerline Communications (PLC), and others. The Internet is an exampleof a WAN that connects disparate networks throughout the world,providing global communication between nodes on various networks. Othertypes of networks, such as field area networks (FANs), neighborhood areanetworks (NANs), personal area networks (PANs), enterprise networks,etc. may also make up the components of any given computer network.

The nodes typically communicate over the network by exchanging discreteframes or packets of data according to predefined protocols, such as theTransmission Control Protocol/Internet Protocol (TCP/IP). In thiscontext, a protocol consists of a set of rules defining how the nodesinteract with each other. Computer networks may be furtherinterconnected by an intermediate network node, such as a router, toextend the effective “size” of each network.

Smart object networks, such as sensor networks, in particular, are aspecific type of network having spatially distributed autonomous devicessuch as sensors, actuators, etc., that cooperatively monitor physical orenvironmental conditions at different locations, such as, e.g.,energy/power consumption, resource consumption (e.g., water/gas/etc. foradvanced metering infrastructure or “AMI” applications) temperature,pressure, vibration, sound, radiation, motion, pollutants, etc. Othertypes of smart objects include actuators, e.g., responsible for turningon/off an engine or perform any other actions. Sensor networks, a typeof smart object network, are typically shared-media networks, such aswireless or power-line communication networks. That is, in addition toone or more sensors, each sensor device (node) in a sensor network maygenerally be equipped with a radio transceiver or other communicationport, a microcontroller, and an energy source, such as a battery.Generally, size and cost constraints on smart object nodes (e.g.,sensors) result in corresponding constraints on resources such asenergy, memory, computational speed and bandwidth.

FIG. 1A is a schematic block diagram of an example computer network 100illustratively comprising nodes/devices, such as a plurality ofrouters/devices interconnected by links or networks, as shown. Forexample, customer edge (CE) routers 110 may be interconnected withprovider edge (PE) routers 120 (e.g., PE-1, PE-2, and PE-3) in order tocommunicate across a core network, such as an illustrative networkbackbone 130. For example, routers 110, 120 may be interconnected by thepublic Internet, a multiprotocol label switching (MPLS) virtual privatenetwork (VPN), or the like. Data packets 140 (e.g., traffic/messages)may be exchanged among the nodes/devices of the computer network 100over links using predefined network communication protocols such as theTransmission Control Protocol/Internet Protocol (TCP/IP), User DatagramProtocol (UDP), Asynchronous Transfer Mode (ATM) protocol, Frame Relayprotocol, or any other suitable protocol. Those skilled in the art willunderstand that any number of nodes, devices, links, etc. may be used inthe computer network, and that the view shown herein is for simplicity.

In some implementations, a router or a set of routers may be connectedto a private network (e.g., dedicated leased lines, an optical network,etc.) or a virtual private network (VPN), such as an MPLS VPN thanks toa carrier network, via one or more links exhibiting very differentnetwork and service level agreement characteristics.

FIG. 1B illustrates an example of network 100 in greater detail,according to various embodiments. As shown, network backbone 130 mayprovide connectivity between devices located in different geographicalareas and/or different types of local networks. For example, network 100may comprise local/branch networks 160, 162 that include devices/nodes10-16 and devices/nodes 18-20, respectively, as well as a datacenter/cloud environment 150 that includes servers 152-154. Notably,local networks 160-162 and data center/cloud environment 150 may belocated in different geographic locations. Servers 152-154 may include,in various embodiments, any number of suitable servers or othercloud-based resources. As would be appreciated, network 100 may includeany number of local networks, data centers, cloud environments,devices/nodes, servers, etc.

In some embodiments, the techniques herein may be applied to othernetwork topologies and configurations. For example, the techniquesherein may be applied to peering points with high-speed links, datacenters, etc. Furthermore, in various embodiments, network 100 mayinclude one or more mesh networks, such as an Internet of Thingsnetwork. Loosely, the term “Internet of Things” or “IoT” refers touniquely identifiable objects (things) and their virtual representationsin a network-based architecture. In particular, the next frontier in theevolution of the Internet is the ability to connect more than justcomputers and communications devices, but rather the ability to connect“objects” in general, such as lights, appliances, vehicles, heating,ventilating, and air-conditioning (HVAC), windows and window shades andblinds, doors, locks, etc. The “Internet of Things” thus generallyrefers to the interconnection of objects (e.g., smart objects), such assensors and actuators, over a computer network (e.g., via IP), which maybe the public Internet or a private network.

Notably, shared-media mesh networks, such as wireless networks, areoften on what is referred to as Low-Power and Lossy Networks (LLNs),which are a class of network in which both the routers and theirinterconnect are constrained: LLN routers typically operate withconstraints, e.g., processing power, memory, and/or energy (battery),and their interconnects are characterized by, illustratively, high lossrates, low data rates, and/or instability. LLNs are comprised ofanything from a few dozen to thousands or even millions of LLN routers,and support point-to-point traffic (between devices inside the LLN),point-to-multipoint traffic (from a central control point such at theroot node to a subset of devices inside the LLN), andmultipoint-to-point traffic (from devices inside the LLN towards acentral control point). Often, an IoT network is implemented with anLLN-like architecture. For example, as shown, local network 160 may bean LLN in which CE-2 operates as a root node for nodes/devices 10-16 inthe local mesh, in some embodiments.

FIG. 2 is a schematic block diagram of an example computing device(e.g., apparatus) 200 that may be used with one or more embodimentsdescribed herein, e.g., as any of the devices shown in FIGS. 1A-1Babove, and particularly as specific devices as described further below.The device may comprise one or more network interfaces 210 (e.g., wired,wireless, etc.), at least one processor 220, and a memory 240interconnected by a system bus 250, as well as a power supply 260 (e.g.,battery, plug-in, etc.).

The network interface(s) 210 contain the mechanical, electrical, andsignaling circuitry for communicating data over links coupled to thenetwork 100, e.g., providing a data connection between device 200 andthe data network, such as the Internet. The network interfaces may beconfigured to transmit and/or receive data using a variety of differentcommunication protocols. For example, interfaces 210 may include wiredtransceivers, wireless transceivers, cellular transceivers, or the like,each to allow device 200 to communicate information to and from a remotecomputing device or server over an appropriate network. The same networkinterfaces 210 also allow communities of multiple devices 200 tointerconnect among themselves, either peer-to-peer, or up and down ahierarchy. Note, further, that the nodes may have two different types ofnetwork connections 210, e.g., wireless and wired/physical connections,and that the view herein is merely for illustration. Also, while thenetwork interface 210 is shown separately from power supply 260, fordevices using powerline communication (PLC) or Power over Ethernet(PoE), the network interface 210 may communicate through the powersupply 260, or may be an integral component of the power supply.

The memory 240 comprises a plurality of storage locations that areaddressable by the processor 220 and the network interfaces 210 forstoring software programs and data structures associated with theembodiments described herein. The processor 220 may comprise hardwareelements or hardware logic adapted to execute the software programs andmanipulate the data structures 245. An operating system 242, portions ofwhich are typically resident in memory 240 and executed by theprocessor, functionally organizes the device by, among other things,invoking operations in support of software processes and/or servicesexecuting on the device. These software processes and/or services maycomprise one or more functional processes 246, and on certain devices,an illustrative “Single Page App Monitoring” process 248, as describedherein. Notably, functional processes 246, when executed by processor(s)220, cause each particular device 200 to perform the various functionscorresponding to the particular device's purpose and generalconfiguration. For example, a router would be configured to operate as arouter, a server would be configured to operate as a server, an accesspoint (or gateway) would be configured to operate as an access point (orgateway), a client device would be configured to operate as a clientdevice, and so on.

It will be apparent to those skilled in the art that other processor andmemory types, including various computer-readable media, may be used tostore and execute program instructions pertaining to the techniquesdescribed herein. Also, while the description illustrates variousprocesses, it is expressly contemplated that various processes may beembodied as modules configured to operate in accordance with thetechniques herein (e.g., according to the functionality of a similarprocess). Further, while the processes have been shown separately, thoseskilled in the art will appreciate that processes may be routines ormodules within other processes.

—Application Intelligence Platform—

The embodiments herein relate to an application intelligence platformfor application performance management. In one aspect, as discussed withrespect to FIGS. 3-5 below, performance within a networking environmentmay be monitored, specifically by monitoring applications and entities(e.g., transactions, tiers, nodes, and machines) in the networkingenvironment using agents installed at individual machines at theentities. As an example, applications may be configured to run on one ormore machines (e.g., a customer will typically run one or more nodes ona machine, where an application consists of one or more tiers, and atier consists of one or more nodes). The agents collect data associatedwith the applications of interest and associated nodes and machineswhere the applications are being operated. Examples of the collecteddata may include performance data (e.g., metrics, metadata, etc.) andtopology data (e.g., indicating relationship information). Theagent-collected data may then be provided to one or more servers orcontrollers to analyze the data.

FIG. 3 is a block diagram of an example application intelligenceplatform 300 that can implement one or more aspects of the techniquesherein. The application intelligence platform is a system that monitorsand collects metrics of performance data for an application environmentbeing monitored. At the simplest structure, the application intelligenceplatform includes one or more agents 310 and one or moreservers/controllers 320. Note that while FIG. 3 shows four agents (e.g.,Agent 1 through Agent 4) communicatively linked to a single controller,the total number of agents and controllers can vary based on a number offactors including the number of applications monitored, how distributedthe application environment is, the level of monitoring desired, thelevel of user experience desired, and so on.

The controller 320 is the central processing and administration serverfor the application intelligence platform. The controller 320 serves abrowser-based user interface (UI) 330 that is the primary interface formonitoring, analyzing, and troubleshooting the monitored environment.The controller 320 can control and manage monitoring of businesstransactions (described below) distributed over application servers.Specifically, the controller 320 can receive runtime data from agents310 (and/or other coordinator devices), associate portions of businesstransaction data, communicate with agents to configure collection ofruntime data, and provide performance data and reporting through theinterface 330. The interface 330 may be viewed as a web-based interfaceviewable by a client device 340. In some implementations, a clientdevice 340 can directly communicate with controller 320 to view aninterface for monitoring data. The controller 320 can include avisualization system 350 for displaying the reports and dashboardsrelated to the disclosed technology. In some implementations, thevisualization system 350 can be implemented in a separate machine (e.g.,a server) different from the one hosting the controller 320.

Notably, in an illustrative Software as a Service (SaaS) implementation,a controller instance 320 may be hosted remotely by a provider of theapplication intelligence platform 300. In an illustrative on-premises(On-Prem) implementation, a controller instance 320 may be installedlocally and self-administered.

The controllers 320 receive data from different agents 310 (e.g., Agents1-4) deployed to monitor applications, databases and database servers,servers, and end user clients for the monitored environment. Any of theagents 310 can be implemented as different types of agents with specificmonitoring duties. For example, application agents may be installed oneach server that hosts applications to be monitored. Instrumenting anagent adds an application agent into the runtime process of theapplication.

Database agents, for example, may be software (e.g., a Java program)installed on a machine that has network access to the monitoreddatabases and the controller. Database agents query the monitoreddatabases in order to collect metrics and pass those metrics along fordisplay in a metric browser (e.g., for database monitoring and analysiswithin databases pages of the controller's UI 330). Multiple databaseagents can report to the same controller. Additional database agents canbe implemented as backup database agents to take over for the primarydatabase agents during a failure or planned machine downtime. Theadditional database agents can run on the same machine as the primaryagents or on different machines. A database agent can be deployed ineach distinct network of the monitored environment. Multiple databaseagents can run under different user accounts on the same machine.

Standalone machine agents, on the other hand, may be standalone programs(e.g., standalone Java programs) that collect hardware-relatedperformance statistics from the servers (or other suitable devices) inthe monitored environment. The standalone machine agents can be deployedon machines that host application servers, database servers, messagingservers, Web servers, etc. A standalone machine agent has an extensiblearchitecture (e.g., designed to accommodate changes).

End user monitoring (EUM) may be performed using browser agents andmobile agents to provide performance information from the point of viewof the client, such as a web browser or a mobile native application.Through EUM, web use, mobile use, or combinations thereof (e.g., by realusers or synthetic agents) can be monitored based on the monitoringneeds. Notably, browser agents (e.g., agents 310) can include Reportersthat report monitored data to the controller.

Monitoring through browser agents and mobile agents are generally unlikemonitoring through application agents, database agents, and standalonemachine agents that are on the server. In particular, browser agents maygenerally be embodied as small files using web-based technologies, suchas JavaScript agents injected into each instrumented web page (e.g., asclose to the top as possible) as the web page is served, and areconfigured to collect data. Once the web page has completed loading, thecollected data may be bundled into a beacon and sent to an EUMprocess/cloud for processing and made ready for retrieval by thecontroller. Browser real user monitoring (Browser RUM) provides insightsinto the performance of a web application from the point of view of areal or synthetic end user. For example, Browser RUM can determine howspecific Ajax or iframe calls are slowing down page load time and howserver performance impact end user experience in aggregate or inindividual cases.

A mobile agent, on the other hand, may be a small piece of highlyperformant code that gets added to the source of the mobile application.Mobile RUM provides information on the native mobile application (e.g.,iOS or Android applications) as the end users actually use the mobileapplication. Mobile RUM provides visibility into the functioning of themobile application itself and the mobile application's interaction withthe network used and any server-side applications with which the mobileapplication communicates.

Application Intelligence Monitoring: The disclosed technology canprovide application intelligence data by monitoring an applicationenvironment that includes various services such as web applicationsserved from an application server (e.g., Java virtual machine (JVM),Internet Information Services (IIS), Hypertext Preprocessor (PHP) Webserver, etc.), databases or other data stores, and remote services suchas message queues and caches. The services in the applicationenvironment can interact in various ways to provide a set of cohesiveuser interactions with the application, such as a set of user servicesapplicable to end user customers.

Application Intelligence Modeling: Entities in the applicationenvironment (such as the JBoss service, MQSeries modules, and databases)and the services provided by the entities (such as a login transaction,service or product search, or purchase transaction) may be mapped to anapplication intelligence model. In the application intelligence model, abusiness transaction represents a particular service provided by themonitored environment. For example, in an e-commerce application,particular real-world services can include a user logging in, searchingfor items, or adding items to the cart. In a content portal, particularreal-world services can include user requests for content such assports, business, or entertainment news. In a stock trading application,particular real-world services can include operations such as receivinga stock quote, buying, or selling stocks.

Business Transactions: A business transaction representation of theparticular service provided by the monitored environment provides a viewon performance data in the context of the various tiers that participatein processing a particular request. A business transaction, which mayeach be identified by a unique business transaction identification (ID),represents the end-to-end processing path used to fulfill a servicerequest in the monitored environment (e.g., adding items to a shoppingcart, storing information in a database, purchasing an item online,etc.). Thus, a business transaction is a type of user-initiated actionin the monitored environment defined by an entry point and a processingpath across application servers, databases, and potentially many otherinfrastructure components. Each instance of a business transaction is anexecution of that transaction in response to a particular user request(e.g., a socket call, illustratively associated with the TCP layer). Abusiness transaction can be created by detecting incoming requests at anentry point and tracking the activity associated with request at theoriginating tier and across distributed components in the applicationenvironment (e.g., associating the business transaction with a 4-tupleof a source IP address, source port, destination IP address, anddestination port). A flow map can be generated for a businesstransaction that shows the touch points for the business transaction inthe application environment. In one embodiment, a specific tag may beadded to packets by application specific agents for identifying businesstransactions (e.g., a custom header field attached to an HTTP payload byan application agent, or by a network agent when an application makes aremote socket call), such that packets can be examined by network agentsto identify the business transaction identifier (ID) (e.g., a GloballyUnique Identifier (GUID) or Universally Unique Identifier (UUID)).

Performance monitoring can be oriented by business transaction to focuson the performance of the services in the application environment fromthe perspective of end users. Performance monitoring based on businesstransactions can provide information on whether a service is available(e.g., users can log in, check out, or view their data), response timesfor users, and the cause of problems when the problems occur.

A business application is the top-level container in the applicationintelligence model. A business application contains a set of relatedservices and business transactions. In some implementations, a singlebusiness application may be needed to model the environment. In someimplementations, the application intelligence model of the applicationenvironment can be divided into several business applications. Businessapplications can be organized differently based on the specifics of theapplication environment. One consideration is to organize the businessapplications in a way that reflects work teams in a particularorganization, since role-based access controls in the Controller UI areoriented by business application.

A node in the application intelligence model corresponds to a monitoredserver or JVM in the application environment. A node is the smallestunit of the modeled environment. In general, a node corresponds to anindividual application server, JVM, or Common Language Runtime (CLR) onwhich a monitoring Agent is installed. Each node identifies itself inthe application intelligence model. The Agent installed at the node isconfigured to specify the name of the node, tier, and businessapplication under which the Agent reports data to the Controller.

Business applications contain tiers, the unit in the applicationintelligence model that includes one or more nodes. Each node representsan instrumented service (such as a web application). While a node can bea distinct application in the application environment, in theapplication intelligence model, a node is a member of a tier, which,along with possibly many other tiers, make up the overall logicalbusiness application.

Tiers can be organized in the application intelligence model dependingon a mental model of the monitored application environment. For example,identical nodes can be grouped into a single tier (such as a cluster ofredundant servers). In some implementations, any set of nodes, identicalor not, can be grouped for the purpose of treating certain performancemetrics as a unit into a single tier.

The traffic in a business application flows among tiers and can bevisualized in a flow map using lines among tiers. In addition, the linesindicating the traffic flows among tiers can be annotated withperformance metrics. In the application intelligence model, there maynot be any interaction among nodes within a single tier. Also, in someimplementations, an application agent node cannot belong to more thanone tier. Similarly, a machine agent cannot belong to more than onetier. However, more than one machine agent can be installed on amachine.

A backend is a component that participates in the processing of abusiness transaction instance. A backend is not instrumented by anagent. A backend may be a web server, database, message queue, or othertype of service. The agent recognizes calls to these backend servicesfrom instrumented code (called exit calls). When a service is notinstrumented and cannot continue the transaction context of the call,the agent determines that the service is a backend component. The agentpicks up the transaction context at the response at the backend andcontinues to follow the context of the transaction from there.

Performance information is available for the backend call. For detailedtransaction analysis for the leg of a transaction processed by thebackend, the database, web service, or other application need to beinstrumented.

The application intelligence platform uses both self-learned baselinesand configurable thresholds to help identify application issues. Acomplex distributed application has a large number of performancemetrics and each metric is important in one or more contexts. In suchenvironments, it is difficult to determine the values or ranges that arenormal for a particular metric; set meaningful thresholds on which tobase and receive relevant alerts; and determine what is a “normal”metric when the application or infrastructure undergoes change. Forthese reasons, the disclosed application intelligence platform canperform anomaly detection based on dynamic baselines or thresholds.

The disclosed application intelligence platform automatically calculatesdynamic baselines for the monitored metrics, defining what is “normal”for each metric based on actual usage. The application intelligenceplatform uses these baselines to identify subsequent metrics whosevalues fall out of this normal range. Static thresholds that are tediousto set up and, in rapidly changing application environments,error-prone, are no longer needed.

The disclosed application intelligence platform can use configurablethresholds to maintain service level agreements (SLAs) and ensureoptimum performance levels for system by detecting slow, very slow, andstalled transactions. Configurable thresholds provide a flexible way toassociate the right business context with a slow request to isolate theroot cause.

In addition, health rules can be set up with conditions that use thedynamically generated baselines to trigger alerts or initiate othertypes of remedial actions when performance problems are occurring or maybe about to occur.

For example, dynamic baselines can be used to automatically establishwhat is considered normal behavior for a particular application.Policies and health rules can be used against baselines or other healthindicators for a particular application to detect and troubleshootproblems before users are affected. Health rules can be used to definemetric conditions to monitor, such as when the “average response time isfour times slower than the baseline”. The health rules can be createdand modified based on the monitored application environment.

Examples of health rules for testing business transaction performancecan include business transaction response time and business transactionerror rate. For example, health rule that tests whether the businesstransaction response time is much higher than normal can define acritical condition as the combination of an average response timegreater than the default baseline by 3 standard deviations and a loadgreater than 50 calls per minute. In some implementations, this healthrule can define a warning condition as the combination of an averageresponse time greater than the default baseline by 2 standard deviationsand a load greater than 100 calls per minute. In some implementations,the health rule that tests whether the business transaction error rateis much higher than normal can define a critical condition as thecombination of an error rate greater than the default baseline by 3standard deviations and an error rate greater than 10 errors per minuteand a load greater than 50 calls per minute. In some implementations,this health rule can define a warning condition as the combination of anerror rate greater than the default baseline by 2 standard deviationsand an error rate greater than 5 errors per minute and a load greaterthan 50 calls per minute. These are non-exhaustive and non-limitingexamples of health rules and other health rules can be defined asdesired by the user.

Policies can be configured to trigger actions when a health rule isviolated or when any event occurs. Triggered actions can includenotifications, diagnostic actions, auto-scaling capacity, runningremediation scripts.

Most of the metrics relate to the overall performance of the applicationor business transaction (e.g., load, average response time, error rate,etc.) or of the application server infrastructure (e.g., percentage CPUbusy, percentage of memory used, etc.). The Metric Browser in thecontroller UI can be used to view all of the metrics that the agentsreport to the controller.

In addition, special metrics called information points can be created toreport on how a given business (as opposed to a given application) isperforming. For example, the performance of the total revenue for acertain product or set of products can be monitored. Also, informationpoints can be used to report on how a given code is performing, forexample how many times a specific method is called and how long it istaking to execute. Moreover, extensions that use the machine agent canbe created to report user defined custom metrics. These custom metricsare base-lined and reported in the controller, just like the built-inmetrics.

All metrics can be accessed programmatically using a RepresentationalState Transfer (REST) API that returns either the JavaScript ObjectNotation (JSON) or the eXtensible Markup Language (XML) format. Also,the REST API can be used to query and manipulate the applicationenvironment.

Snapshots provide a detailed picture of a given application at a certainpoint in time. Snapshots usually include call graphs that allow thatenables drilling down to the line of code that may be causingperformance problems. The most common snapshots are transactionsnapshots.

FIG. 4 illustrates an example application intelligence platform (system)400 for performing one or more aspects of the techniques herein. Thesystem 400 in FIG. 4 includes client device 405 and 492, mobile device415, network 420, network server 425, application servers 430, 440, 450,and 460, asynchronous network machine 470, data stores 480 and 485,controller 490, and data collection server 495. The controller 490 caninclude visualization system 496 for providing displaying of the reportgenerated for performing the field name recommendations for fieldextraction as disclosed in the present disclosure. In someimplementations, the visualization system 496 can be implemented in aseparate machine (e.g., a server) different from the one hosting thecontroller 490.

Client device 405 may include network browser 410 and be implemented asa computing device, such as for example a laptop, desktop, workstation,or some other computing device. Network browser 410 may be a clientapplication for viewing content provided by an application server, suchas application server 430 via network server 425 over network 420.

Network browser 410 may include agent 412. Agent 412 may be installed onnetwork browser 410 and/or client 405 as a network browser add-on,downloading the application to the server, or in some other manner.Agent 412 may be executed to monitor network browser 410, the operatingsystem of client 405, and any other application, API, or anothercomponent of client 405. Agent 412 may determine network browsernavigation timing metrics, access browser cookies, monitor code, andtransmit data to data collection 460, controller 490, or another device.Agent 412 may perform other operations related to monitoring a requestor a network at client 405 as discussed herein including reportgenerating.

Mobile device 415 is connected to network 420 and may be implemented asa portable device suitable for sending and receiving content over anetwork, such as for example a mobile phone, smart phone, tabletcomputer, or other portable device. Both client device 405 and mobiledevice 415 may include hardware and/or software configured to access aweb service provided by network server 425.

Mobile device 415 may include network browser 417 and an agent 419.Mobile device may also include client applications and other code thatmay be monitored by agent 419. Agent 419 may reside in and/orcommunicate with network browser 417, as well as communicate with otherapplications, an operating system, APIs and other hardware and softwareon mobile device 415. Agent 419 may have similar functionality as thatdescribed herein for agent 412 on client 405, and may report data todata collection server 460 and/or controller 490.

Network 420 may facilitate communication of data among differentservers, devices and machines of system 400 (some connections shown withlines to network 420, some not shown). The network may be implemented asa private network, public network, intranet, the Internet, a cellularnetwork, Wi-Fi network, VoIP network, or a combination of one or more ofthese networks. The network 420 may include one or more machines such asload balance machines and other machines.

Network server 425 is connected to network 420 and may receive andprocess requests received over network 420. Network server 425 may beimplemented as one or more servers implementing a network service, andmay be implemented on the same machine as application server 430 or oneor more separate machines. When network 420 is the Internet, networkserver 425 may be implemented as a web server.

Application server 430 communicates with network server 425, applicationservers 440 and 450, and controller 490. Application server 450 may alsocommunicate with other machines and devices (not illustrated in FIG. 3).Application server 430 may host an application or portions of adistributed application. The host application 432 may be in one of manyplatforms, such as including a Java, PHP, .Net, and Node.JS, beimplemented as a Java virtual machine, or include some other host type.Application server 430 may also include one or more agents 434 (i.e.,“modules”), including a language agent, machine agent, and networkagent, and other software modules. Application server 430 may beimplemented as one server or multiple servers as illustrated in FIG. 4.

Application 432 and other software on application server 430 may beinstrumented using byte code insertion, or byte code instrumentation(BCI), to modify the object code of the application or other software.The instrumented object code may include code used to detect callsreceived by application 432, calls sent by application 432, andcommunicate with agent 434 during execution of the application. BCI mayalso be used to monitor one or more sockets of the application and/orapplication server in order to monitor the socket and capture packetscoming over the socket.

In some embodiments, server 430 may include applications and/or codeother than a virtual machine. For example, servers 430, 440, 450, and460 may each include Java code, .Net code, PHP code, Ruby code, C code,C++ or other binary code to implement applications and process requestsreceived from a remote source. References to a virtual machine withrespect to an application server are intended to be for exemplarypurposes only.

Agents 434 on application server 430 may be installed, downloaded,embedded, or otherwise provided on application server 430. For example,agents 434 may be provided in server 430 by instrumentation of objectcode, downloading the agents to the server, or in some other manner.Agent 434 may be executed to monitor application server 430, monitorcode running in a virtual machine 432 (or other program language, suchas a PHP, .Net, or C program), machine resources, network layer data,and communicate with byte instrumented code on application server 430and one or more applications on application server 430.

Each of agents 434, 444, 454, and 464 may include one or more agents,such as language agents, machine agents, and network agents. A languageagent may be a type of agent that is suitable to run on a particularhost. Examples of language agents include a Java agent, .Net agent, PHPagent, and other agents. The machine agent may collect data from aparticular machine on which it is installed. A network agent may capturenetwork information, such as data collected from a socket.

Agent 434 may detect operations such as receiving calls and sendingrequests by application server 430, resource usage, and incomingpackets. Agent 434 may receive data, process the data, for example byaggregating data into metrics, and transmit the data and/or metrics tocontroller 490. Agent 434 may perform other operations related tomonitoring applications and application server 430 as discussed herein.For example, agent 434 may identify other applications, share businesstransaction data, aggregate detected runtime data, and other operations.

An agent may operate to monitor a node, tier of nodes, or other entity.A node may be a software program or a hardware component (e.g., memory,processor, and so on). A tier of nodes may include a plurality of nodeswhich may process a similar business transaction, may be located on thesame server, may be associated with each other in some other way, or maynot be associated with each other.

A language agent may be an agent suitable to instrument or modify,collect data from, and reside on a host. The host may be a Java, PHP,.Net, Node.JS, or other type of platform. Language agents may collectflow data as well as data associated with the execution of a particularapplication. The language agent may instrument the lowest level of theapplication to gather the flow data. The flow data may indicate whichtier is communicating with which tier and on which port. In someinstances, the flow data collected from the language agent includes asource IP, a source port, a destination IP, and a destination port. Thelanguage agent may report the application data and call chain data to acontroller. The language agent may report the collected flow dataassociated with a particular application to a network agent.

A network agent may be a standalone agent that resides on the host andcollects network flow group data. The network flow group data mayinclude a source IP, destination port, destination IP, and protocolinformation for network flow received by an application on which networkagent is installed. The network agent may collect data by interceptingand performing packet capture on packets coming in from one or morenetwork interfaces (e.g., so that data generated/received by all theapplications using sockets can be intercepted). The network agent mayreceive flow data from a language agent that is associated withapplications to be monitored. For flows in the flow group data thatmatch flow data provided by the language agent, the network agent rollsup the flow data to determine metrics such as TCP throughput, TCP loss,latency, and bandwidth. The network agent may then report the metrics,flow group data, and call chain data to a controller. The network agentmay also make system calls at an application server to determine systeminformation, such as for example a host status check, a network statuscheck, socket status, and other information.

A machine agent, which may be referred to as an infrastructure agent,may reside on the host and collect information regarding the machinewhich implements the host. A machine agent may collect and generatemetrics from information such as processor usage, memory usage, andother hardware information.

Each of the language agent, network agent, and machine agent may reportdata to the controller. Controller 490 may be implemented as a remoteserver that communicates with agents located on one or more servers ormachines. The controller may receive metrics, call chain data and otherdata, correlate the received data as part of a distributed transaction,and report the correlated data in the context of a distributedapplication implemented by one or more monitored applications andoccurring over one or more monitored networks. The controller mayprovide reports, one or more user interfaces, and other information fora user.

Agent 434 may create a request identifier for a request received byserver 430 (for example, a request received by a client 405 or 415associated with a user or another source). The request identifier may besent to client 405 or mobile device 415, whichever device sent therequest. In embodiments, the request identifier may be created when datais collected and analyzed for a particular business transaction.

Each of application servers 440, 450, and 460 may include an applicationand agents. Each application may run on the corresponding applicationserver. Each of applications 442, 452, and 462 on application servers440-460 may operate similarly to application 432 and perform at least aportion of a distributed business transaction. Agents 444, 454, and 464may monitor applications 442-462, collect and process data at runtime,and communicate with controller 490. The applications 432, 442, 452, and462 may communicate with each other as part of performing a distributedtransaction. Each application may call any application or method ofanother virtual machine.

Asynchronous network machine 470 may engage in asynchronouscommunications with one or more application servers, such as applicationserver 450 and 460. For example, application server 450 may transmitseveral calls or messages to an asynchronous network machine. Ratherthan communicate back to application server 450, the asynchronousnetwork machine may process the messages and eventually provide aresponse, such as a processed message, to application server 460.Because there is no return message from the asynchronous network machineto application server 450, the communications among them areasynchronous.

Data stores 480 and 485 may each be accessed by application servers suchas application server 450. Data store 485 may also be accessed byapplication server 450. Each of data stores 480 and 485 may store data,process data, and return queries received from an application server.Each of data stores 480 and 485 may or may not include an agent.

Controller 490 may control and manage monitoring of businesstransactions distributed over application servers 430-460. In someembodiments, controller 490 may receive application data, including dataassociated with monitoring client requests at client 405 and mobiledevice 415, from data collection server 460. In some embodiments,controller 490 may receive application monitoring data and network datafrom each of agents 412, 419, 434, 444, and 454 (also referred to hereinas “application monitoring agents”). Controller 490 may associateportions of business transaction data, communicate with agents toconfigure collection of data, and provide performance data and reportingthrough an interface. The interface may be viewed as a web-basedinterface viewable by client device 492, which may be a mobile device,client device, or any other platform for viewing an interface providedby controller 490. In some embodiments, a client device 492 may directlycommunicate with controller 490 to view an interface for monitoringdata.

Client device 492 may include any computing device, including a mobiledevice or a client computer such as a desktop, work station or othercomputing device. Client computer 492 may communicate with controller390 to create and view a custom interface. In some embodiments,controller 490 provides an interface for creating and viewing the custominterface as a content page, e.g., a web page, which may be provided toand rendered through a network browser application on client device 492.

Applications 432, 442, 452, and 462 may be any of several types ofapplications. Examples of applications that may implement applications432-462 include a Java, PHP, .Net, Node.JS, and other applications.

FIG. 5 is a block diagram of a computer system 500 for implementing thepresent technology, which is a specific implementation of device 200 ofFIG. 2 above. System 500 of FIG. 5 may be implemented in the contexts ofthe likes of clients 405, 492, network server 425, servers 430, 440,450, 460, a synchronous network machine 470, and controller 490 of FIG.4. (Note that the specifically configured system 500 of FIG. 5 and thecustomized device 200 of FIG. 2 are not meant to be mutually exclusive,and the techniques herein may be performed by any suitably configuredcomputing device.)

The computing system 500 of FIG. 5 includes one or more processors 510and memory 520. Main memory 520 stores, in part, instructions and datafor execution by processor 510. Main memory 510 can store the executablecode when in operation. The system 500 of FIG. 5 further includes a massstorage device 530, portable storage medium drive(s) 540, output devices550, user input devices 560, a graphics display 570, and peripheraldevices 580.

The components shown in FIG. 5 are depicted as being connected via asingle bus 590. However, the components may be connected through one ormore data transport means. For example, processor unit 510 and mainmemory 520 may be connected via a local microprocessor bus, and the massstorage device 530, peripheral device(s) 580, portable or remote storagedevice 540, and display system 570 may be connected via one or moreinput/output (I/O) buses.

Mass storage device 530, which may be implemented with a magnetic diskdrive or an optical disk drive, is a non-volatile storage device forstoring data and instructions for use by processor unit 510. Massstorage device 530 can store the system software for implementingembodiments of the present invention for purposes of loading thatsoftware into main memory 520.

Portable storage device 540 operates in conjunction with a portablenon-volatile storage medium, such as a compact disk, digital video disk,magnetic disk, flash storage, etc. to input and output data and code toand from the computer system 500 of FIG. 5. The system software forimplementing embodiments of the present invention may be stored on sucha portable medium and input to the computer system 500 via the portablestorage device 540.

Input devices 560 provide a portion of a user interface. Input devices560 may include an alpha-numeric keypad, such as a keyboard, forinputting alpha-numeric and other information, or a pointing device,such as a mouse, a trackball, stylus, or cursor direction keys.Additionally, the system 500 as shown in FIG. 5 includes output devices550. Examples of suitable output devices include speakers, printers,network interfaces, and monitors.

Display system 570 may include a liquid crystal display (LCD) or othersuitable display device. Display system 570 receives textual andgraphical information, and processes the information for output to thedisplay device.

Peripherals 580 may include any type of computer support device to addadditional functionality to the computer system. For example, peripheraldevice(s) 580 may include a modem or a router.

The components contained in the computer system 500 of FIG. 5 caninclude a personal computer, hand held computing device, telephone,mobile computing device, workstation, server, minicomputer, mainframecomputer, or any other computing device. The computer can also includedifferent bus configurations, networked platforms, multi-processorplatforms, etc. Various operating systems can be used including Unix,Linux, Windows, Apple OS, and other suitable operating systems,including mobile versions.

When implementing a mobile device such as smart phone or tabletcomputer, the computer system 500 of FIG. 5 may include one or moreantennas, radios, and other circuitry for communicating over wirelesssignals, such as for example communication using Wi-Fi, cellular, orother wireless signals.

—Determining Start Times and Cause-Based Event Correlation for VirtualPage Transitions in SPAs—

As mentioned above, Single Page Applications (SPAs) typically load aninitial page first, and then when a certain event occurs (e.g., a userdoes something) to navigate to another page, the web browser applicationdoes not reload the entire page, but only some codes, data, resources,etc., to load a virtual page (or virtual pageview).

Notably, there are often some resources that are downloaded for a newvirtual page that actually began or completed before a URL change.Unlike traditional pages, however, the start of a virtual page is notthe URL change but the time at which some cause occurred to initiate thevirtual page (e.g., user interaction events or otherwise). For example,when a user clicks something (e.g., a link, an icon, a button, etc.) ona page and resources begin to load for a new virtual page, the time atwhich the user clicked should be the start time for that new virtualpage. In traditional monitoring, however, since any resource loading,such as XMLHttpRequests (XHRs), happen before the URL changes, thoseresources will be linked to the old virtual page, which is incorrect,and the start time of the virtual page is set to the time of the URLchange, which is inaccurate.

Certain techniques herein, therefore, provide for a “causality chaining”method that will link any loaded resources (e.g., XHR) to their rootcause (e.g., user interactions or otherwise), where if a URL thenchanges (a new virtual page), those resources are correctly related tothe newly loaded virtual page, and a more accurate start time can bemarked as the root cause (e.g., the user's click), rather than the URLchange itself. In particular, since XHRs play a large role in pagetransitions in SPAs, correlating the XHRs to transitions (virtual pages)provides an important picture to the site owner about the overallperformance of the virtual pages. Accordingly, it would not be accurateif the correlation is simply based on the time that the virtual pagestarts loading, as noted above.

Consider, for example, a common case illustrated with reference to thetimeline 600 of FIG. 6, where during virtual page 1, two XHRs 610 a and610 b begin loading the content of the next virtual page 2. The systemmarks a browser history change (e.g., URL change) in its callbackfunction after receiving all response data from the first XHR 610 a.Here, correlating XHRs based on their occurrence after the next virtualpage 2 loads mistakenly correlates XHR 610 a, which is actuallycontributing to the next virtual page 2, to the previous virtual page 1.Additionally, XHR 610 b occurring on both sides of the page transitioncan further result in inaccurate or incorrect measurements.

Accordingly, correlating XHRs simply by when they occur in time andwhich virtual page is operational at that time will inaccuratelymisrepresent a large population of XHRs, which occur right before thenext virtual page transition and directly provide content for thatvirtual page. The techniques herein, therefore, address this problem aswell, and correctly correlate the XHRs to provide an accurate picture ofwhich XHRs relate to which virtual page. For instance, as describedbelow, XHRs that are both causing an SPA transition and contributingafter the SPA transition can be correctly correlated to the virtual pageto which they belong.

Specifically, according to one or more embodiments of the disclosure asdescribed in detail below, a monitoring process detects one or moreevents capable of causing a future state change in a browser applicationhaving initially loaded a single page application (SPA) page, andmaintains one or more causality chains of the one or more events, eachcausality chain tracing events of that causality chain to a respectiveroot cause event of that causality chain. Upon detecting a virtual pagetransition to create a new virtual page, the monitoring process maydetermine that a cause of the virtual page transition matches aparticular root cause event of a particular causality chain, andcorrelates all events of the particular causality chain to the newvirtual page (e.g., where events may notably include Extensible MarkupLanguage Hypertext Transfer Protocol Requests (XHRs)). According to oneor more additional embodiments of the disclosure, the monitoring processmay further set a start time of the particular root cause event as thestart time of the new virtual page.

Operationally, a newly defined “causality chain” technique can be usedto determine the cause and start time of transitions. As detailedfurther below, a causality chain is a stack-like data structure, used tostore a chain of detected events. In particular, when an event happensthat is capable of causing a future state change in a web browser (a“transition”), the system herein stores (pushes) the events and theirstart times in the causality chain (e.g., storing a beginning state/URLand ending state/URL of the transition, and the cause or “trigger” ofthe event, including any parental relation to a previous event thatcaused the stored event). If the event does not trigger a transition orwhen the transition completes, the event will be popped out from(removed from) the causality chain.

Generally, there are four types of events may cause the state of webapplications transition:

-   -   i) user interactions (e.g., explicit user activity such as key        presses, mouse or finger clicks, scrolls, etc.);    -   ii) timer functions such as intervals or other scheduled event        firings (e.g., setTimeout, setInterval, etc.);    -   iii) window messages (e.g., postMessage, receiveMessage, etc.);        and    -   iv) XMLHttpRequests (XHRs) or other callbacks (e.g., xhr.onload,        xhr.onreadystatechange, xhr.send, xhr.open, etc.).        Others may also be or become available, and the list above is        not meant to be limiting to the scope of the embodiments herein.

Recall from above that improper timing associations for page start timescan occur on either side of a transition when inadequate time markersare used. For example, and with reference to the timeline 700 of FIG. 7,when a user clicks a button (705), various functions may send requests710 (e.g., XHR1 710 a and XHR2 710 b) and when the requested data isloaded, the URL changes (715). Further requests 710 c may also betriggered to be loaded after the URL change, and may complete before orafter the page is deemed to have been loaded (725) (depending upon whenthe end time is calculated, such as described in greater detail below).Unlike traditional monitoring, which would declare the start time 720 tobe the URL change 715, the techniques herein set the new virtual pagestart time 730 to be the click time 705. Note also, therefore, thatwhile in traditional monitoring, XHR1 and XHR2 (e.g., requests todownload html/css files for the next page before the URL change) wouldbe related to an older virtual page, the techniques herein correctlylink XHR1 and XHR2 to the new virtual page.

According to one or more of the embodiments herein, a technique isdefined that determines the real cause of the XHRs and also the realcause of the virtual page transitions, then correlates the XHRs to thevirtual page when their causes match. This allows for determining a moreaccurate start time for a virtual page transition, accordingly.

In particular, in order to determine these causes, recall that after theinitial page load, the state of a browser application can only be bycertain actions (e.g., user action, time-based events, window messages,XHR callbacks, etc.). Therefore, the techniques herein specificallymonitor for every such event or function call to record a possible causeof a future change. From these events, the techniques herein can thuscreate a causal event that can be stored and tracked (e.g., pushed intoand popped out of a stack around the event handler of the event). Inthis manner, the techniques herein essentially “wrap” the event handlerwith the causal event.

With reference to the wrapping function 800 shown in FIG. 8, using a“click” event 805 as an example, if there is an event handler 810 orcallback function from the click event, the techniques herein wrap theevent handler 810 with a snippet to push the “click” 805 into the CausalEvent Stack 815 before the event handler 810 (e.g., on top of otherevents 807 a-n). In this manner, during the event handler execution, thetechniques herein can determine that the cause of the handler was the“click” 805 by fetching the top of the Causal Event Stack 815. Uponcompletion of the event handler execution, the “click” is popped fromthe Causal Event Stack 815 (shown as action 820).

Notably, and with reference to FIG. 9, there are instances where oneevent causes another event to happen, in what is referred to herein as a“causality chain” 900. For example, a user's “click” 905 to a button mayillustratively cause an XMLHttpRequest (XHR) 910 to be sent (e.g.,“XHRSend”), where the XHR 910 then causes a timer expiration 915, whichin the end causes a history change and therefore a virtual page 920.These causal events can be chained by their parent property, i.e., thetechniques herein retrieve the parent of an event, allowing back-tracingthe causality chain 900 from the new virtual page 920, by following theparents of each causal event, to determine the root cause of the page920 (e.g., the initial “click” 905).

According to embodiments of the present disclosure, techniques hereinare also defined that can correlate cause-based events, illustrativelyXHRs, to virtual page transitions. For instance, a monitoring service(e.g., an “XHRMonitor”) may be established to continuously monitor XHRactivity. In addition, another monitoring service (or optionally acomponent of the same monitoring service above) may further continuouslymonitor virtual pages (e.g., a “VirtualPageMonitor”) for transitions.Notably, both monitors may optionally be a component of a shared SinglePage App Monitoring process 248, or else may be configured as their ownseparate functions (e.g., individual processes 248).

Referring to FIG. 10A, for example 1000 a, when XHRMonitor 1010 createsan XHR event 1015 (e.g., XHR request), the techniques herein may thencheck the VirtualPageMonitor 1020 for the current virtual page event1025 (e.g., VP Transition). As mentioned above, the causality chain 900may then be traced from each event 1015/1025 to determine the rootcauses of each event. If, as shown in FIG. 10A, the root causes match(e.g., button click 1030), then this XHR event 1015 is correlated to thevirtual page 1025 (e.g., and the XHR event is reported as such).However, if, as shown in example 1000 b of FIG. 10B, the root causes donot match (e.g., button click 1030 corresponds to the YP transition1025, but a timer 1035 initiated the XHR request 1015), then thetechnique continues as described below.

Note that as illustrated in example 1000 c of FIG. 10C, the causalitychain 900 may include multiple chained events (as described above)leading back to the same root cause. Moreover, as shown in example 1000d of FIG. 10D, multiple causality chains 900 a/b can eventually merge tothe same root cause, as well. Any logical trace can occur, and thoseshown herein are merely examples for illustration.

If the root causes do not match, as noted above, the event (e.g., XHRevent 1015) may be placed into a parent virtual page “waiting queue”indexed by a unique ID of the cause event. When a next virtual page 1025is created, the VirtualPageMonitor 1020 will look to the XHRMonitor1010's parent virtual page waiting queue, and uses the cause of thevirtual page to search for the XHR events. If such matching XHR eventsare found, the techniques herein remove them out the waiting queue,correlate them to the new virtual page, and report them.

A demonstration of this is shown in the example 1100 of FIG. 11. A userclicking a button 1105 on virtual page 1 (VP1, e.g., caused by “cause0”) results in a “click” event 1110 (“cause 1”) in VP1. This click isstored in a stack, as described above, and may now cause XHR 1 and XHR 2(XHR events 1115) to be sent out, as well as the transition to virtualpage 2 (VP2). As mentioned above, the cause of XHR 1 and XHR 2 (cause1), would not be the same as virtual page VP1 (cause 0). As such, beforethe transition to virtual page VP2 is finished, XHR 1 and XHR 2 can bematched neither to virtual page VP1 nor to virtual page VP2. Instead,they are pushed into the parent virtual page waiting queue 1120 (alongwith cause 1, the click). Once the transition to virtual page VP2 isfinished, the VirtualPageMonitor can determine its root cause, which isthe “click” (cause 1) in this case, which can then be used as a lookupinto the parent virtual page waiting queue 1120 to find the matchingroot cause (the click) and the associated (i.e., resultant) XHR events 1and 2. Accordingly, the techniques herein may then correlate these XHRevents properly to virtual page VP2.

After a certain period of waiting time (e.g., 2 s), if no new virtualpage is created, all XHR events in the waiting queues are correlated tothe current virtual page (e.g., VP1 above), and reported, such that thewaiting queue can be is cleared. As an example of this, assume that abutton click on the virtual page VP1 does not cause any new virtual pagetransition to VP2, but instead only causes some XHR requests. Since thecurrent virtual page VP1 was not caused by this click (i.e., not bypotential “cause 1”, but instead by “cause 0”), it is not listed as theroot cause. However, according to the techniques herein, the resultantXHRs are correlated to the current virtual page VP1 as they are removedfrom the waiting queue. (That is, the XHR events were waiting to see ifany other virtual pages were generated that may correspond to their rootcause—however since no such relationship exists after some given time,the XHRs may simply be correlated to the current virtual page.)

FIG. 12 illustrates an example simplified procedure for cause-basedevent (e.g., XHR) correlation to virtual page transitions in single pageapplications (SPAs) in accordance with one or more embodiments describedherein. For example, a non-generic, specifically configured device(e.g., device 200) may perform procedure 1200 by executing storedinstructions (e.g., Single Page App Monitoring process 248). Theprocedure 1200 may start at step 1205, and continues to step 1210,where, as described in greater detail above, a monitoring processdetects one or more events capable of causing a future state change in abrowser application having initially loaded an SPA page (e.g., explicituser activity, schedule event firing, window messages, XHRs, etc.). Instep 1215, the monitoring process maintains one or more causality chainsof the one or more events, where, as described above, each causalitychain traces events of that causality chain to a respective root causeevent of that causality chain. Note that when certain events of acausality chain (or standalone events) occur prior to any associatedvirtual page transition, then in step 1220 those specific events may bestored (e.g., in a virtual page waiting queue) until a virtual pagetransition matches the same root cause event of that causality chain.

At the same time, the techniques herein are looking to detect, in step1225, a virtual page transition to create a new virtual page. Once avirtual page is created, then in step 1230 the monitoring processdetermines (e.g., through a lookup/comparison) that a cause of thevirtual page transition matches a particular root cause event of aparticular causality chain from above. Once this match is determined,then in step 1235, the monitoring process may correlate all events ofthat particular causality chain to the new virtual page. Any events thatoccur after the virtual page transition may also be correlated to thatnew virtual page in step 1240. On the other hand, if no virtual pagetransition occurs that creates a new virtual page based on a matchingroot cause event of a given causality chain (e.g., within a giventhreshold of time since a final event of that given causality chainoccurs), then in response, all events of the given causality chain canbe correlated to a current page in step 1245.

According to one or more embodiments herein, in step 1250 the monitoringprocess may also set a start time of the particular root cause event asthe start time of the new virtual page, as described above.

Furthermore, in step 1255, in certain embodiments, one or more actionsmay be performed based on the correlating, such as, e.g., measuring oneor more metrics based on the correlating, detecting and mitigating ananomaly based on the correlating, and so on.

The illustrative and simplified procedure 1200 may then end in step1260.

It should be noted that while certain steps within procedure 1200 may beoptional as described above, the steps shown in FIG. 12 are merelyexamples for illustration, and certain other steps may be included orexcluded as desired. Further, while a particular order of the steps isshown, this ordering is merely illustrative, and any suitablearrangement of the steps may be utilized without departing from thescope of the embodiments herein.

The techniques described herein, therefore, provide cause-based eventcorrelation to virtual page transitions in SPAs, particularly XHRcorrelation. As mentioned above, correlating XHRs simply by when theyoccur in time and which virtual page is functional at that time willinaccurately correlate a large percentage of XHRs, which occur rightbefore the next virtual page transition occurs to directly providecontent for that new virtual page. The techniques herein thus addressthis problem and provide an accurate picture of which XHRs relate towhich virtual page. Illustratively, in a single page application, when auser navigates from one URL to another (i.e., a transition), thistransition time is an important metric in web monitoring. Slowtransition times may be an indicator that the websites are not workingproperly. If the URL change time is used as the transition start time,it is not correct, since the resources and other functions related tothe new virtual page may be running before the URL change. Furthermore,different libraries and frameworks would cause the measurements to beincorrect, as well.

In still further embodiments of the techniques herein, a business impactof virtual page transitions in SPAs can also be quantified. That is,because of issues related to specific applications/processes (e.g., losttraffic, slower servers, overloaded network links, etc.), variouscorresponding business transactions may have been correspondinglyaffected for those applications/processes (e.g., online purchases weredelayed, page visits were halted before fully loading, user satisfactionor dwell time decreased, etc.), while other processes (e.g., on othernetwork segments or at other times) remain unaffected. The techniquesherein, therefore, can correlate the XHRs to their network metrics andwith various business transactions in order to better understand theaffect the XHRs and virtual page transitions may have had on thebusiness transactions, accordingly.

—Determining End Times for SPAs—

As noted above, Single Page Apps (SPAs) have changed the loadingbehavior of web pages, and traditional performance metric monitoringtechniques are inadequate. Historically, for example, websites wouldrequest resources from server and a web page was said to be loaded bythe browser when all the resources had been fetched. With SPAs, however,the web page is not actually loaded completely when browserstraditionally mark it as loaded, because of the different loadingbehavior of SPAs. For instance, browser events like “onload,”“DOMContentLoaded,” etc. do not give accurate metrics for Single PageApps. Also, when a user navigates to a new page, the content may befetched without loading a new page. As such, the browser may be unawareof such pages, and there is currently no mechanism in place to determinewhen such pages have actually loaded.

Certain additional techniques herein, therefore, provide informationthat may be used for accurately calculating end times of different pageloads in Single Page Application (SPA) frameworks.

In particular, Java Script Agents (“JSAgents”), as mentioned above, maybe configured to collect and reports performance data about web pagesand their resources, such as JS, CSS, images, etc. Load Time (“onLoad”),in particular, is a metric obtained when a browser fires an event onLoadwhen a web page is completely loaded. The onLoad time is traditionallydescribed as the time at which all of the objects in the document are inthe Document Object Model (DOM) (a programming interface for HTML andXML documents that represents the page so that programs can change thedocument structure, style, and content), and all the images, scripts,links, and sub-frames have also finished loading.

FIG. 13 represents a time-chart 1300 of network requests 1310 and theirassociated timing 1320 depicting an onLoad time 1330 of a website (anon-SPA website). As can be seen, at onLoad, all of the resources havebeen loaded successfully.

FIG. 14, on the other hand, illustrates an example time-chart 1400 fornetwork requests 1410 of a Single Page App (SPA). Here, the associatedtiming 1420 shows a different story, where the depicted onLoad time 1430is in the middle of the resources being loaded. That is, as can bereadily seen, the “actual load time” 1440 of website is much more thanonLoad time reported by browser.

In particular, as many resources can be fetched through XMLHttpRequests(XHRs) or other events, the resources have not yet finished loading atthe onLoad time 1430 here, and the actual load time 1440 for thiswebsite should be the time at which the last resource (e.g.,“images_photos.jpg”) has been loaded and rendered. To explain why thisis happening, Single Page Apps, because of their design, load resourcedynamically, and the browser is unaware of dynamic resources (e.g.,XHRs) being loaded. Hence, the browser does not accurately report“actual load time” for SPAs, since SPAs also use XHRs to load manyresources on a website, as historically XHRs are never included indetermining the onLoad time (as they are unknown to the browser).

The techniques herein, on the other hand, do determine a more accuratemeasurement of Single Page App end times, and thus load times (e.g.,using the start times calculated above). As described below, by trackingthe resource/XHR loads using load listeners and waiting for all theresources/XHRs to finish loading (i.e., waiting for all the load eventsto finish), the techniques herein can deem a page (a full page or avirtual page) completed by marking that finish time as the end time ofthe page.

Specifically, according to one or more additional or alternativeembodiments of the disclosure as described in detail below, a monitoringprocess detects a page load start time of a single page application(SPA) page having added direct resources and dynamic resources, tracksthe direct resources and dynamic resources, and notes a load end timefor each of the tracked direct resources and dynamic resources. Themonitoring process stops the tracking of the direct resources anddynamic resources in response to a determination of a threshold durationof network inactivity, and determines a maximum load end time of thetracked direct resources and dynamic resources. Accordingly, themonitoring process may then set a page load time of the SPA page as adifference between the maximum load end time and the page load starttime.

Operationally, for determining single page app end times and load times,it should be noted that a resource in a page can either be addeddirectly or dynamically. Since the browser is unaware of the dynamicresources (e.g., XHRs) being added on the page as mentioned above, inorder to determine the “actual load time” of SPA, the techniques hereintrack all of the resources (direct and dynamic/XHRs) loaded on the pageand wait for network inactivity. Once the network is inactive for athreshold amount of time, the time at which network started beinginactive can be set/declared to be the “actual load time”.

According to one or more embodiments of the techniques herein, theillustrative Single Page App Monitoring process 248 may track all of theresources loaded and rendered on the page, and wait for a detectedperiod of network inactivity. Generally, there are three types of directresources to consider:

-   -   i) Images—Tracking load and render time;    -   ii) Scripts—Tracking load time; and    -   iii) Stylesheets—Tracking load time.        Other resources may also be considered, such as fonts or future        types of resources, and those listed here are primary examples        only. Note further that images, in particular, may also be        rendered on a page after loading, and as such, the techniques        herein may specifically consider both load and render time of        images.

To perform an algorithm in accordance with the present disclosure, thetechniques herein may add “hooks” (e.g., event loggers using a JSAgentin DOM) to track any image, script, css (stylesheet), etc. added as adirect resource anytime on a page. The hooks then attach a load listenerto each of these resources, such that when a load listener gets invoked,the techniques herein note the load timestamp of that resource (i.e.,the time at which the resource completed loading or rendering).Accordingly, the resource having the latest/max load timestamp is thelast resource to be loaded/rendered on the page.

The illustrative algorithm can be better explained with reference toFIG. 15, showing a time-chart 1500 of direct resources 1510 loading on apage. In particular, hooks (e.g., added by JSAgent in DOM) trackscripts, stylesheets, and images being added to the page (resources1510), which add a load listener 1515 upon the resource being added topage. When the load listener is invoked for any resource after someassociated timing 1520 of the loading/rendering of the resource, thetechniques herein note the timestamp 1525 of that resource. As mentionedabove, the maximum timestamp (latest time) among all of the timestampsis the direct resource load time 1530 of the page.

As shown in the example 1500, therefore, load listeners have been addedto resources when they are added in DOM:

-   -   bootstrap.min.css loaded at 350 ms;    -   jquery-3.3.1.js loaded at 780 ms; and    -   AppDynamics-Logo_w_500.png loaded at 1.1 s.        Since 1.1 s is the max timestamp among all the direct resources,        the direct resource load time 1530 (“1530-DR”) for this page is        illustratively set/declared to be 1.1 seconds according to the        techniques herein.

Notably, however, since XHRs are also being used by SPAs to loadresources and data for the website, an algorithm is also consideredherein to track XHRs (or other dynamic resources), similarly to thedirect resource tracking above. (Note that in certain embodiments, thedirect and dynamic resources may be tracked by the same algorithm.) Inparticular, hooks may also be added (e.g., by the JSAgent) to trackfiring and loading of XHRs. That is, when any XHR is loaded, note thetimestamp 1525, and the max timestamp among all the timestamps is theXHR load time (“1530-XHR”). (Note further that there may be no need toadd a load listener for XHRs, because XHRs loading can directly betracked by adding hooks to the XHR “load” function, as may beappreciated by those skilled in the art.)

According to the techniques herein, the SPA's Load Time can be computedas the maximum of the load times of the direct and dynamic resources,that is:

SPA Load Time=max(Resource Load Time,XHR Load Time)  Eq. 1.

One important consideration, however, is how to determine when to stoptracking the resources to determine the “last” loaded resource.According to one or more embodiments of the techniques herein, adetermination of network inactivity may be used as an appropriateassumption of page completion (or alternatively, page failure). That is,when there is a network inactiveness of some set (or dynamicallydetermined) threshold of time (e.g., 5 seconds), the techniques hereinmay stop tracking and compute the SPA Load Time under the generallyvalid assumption that the page is loaded (or has failed or stalled).

FIG. 16 illustrates an example timeline 1600 showing a collection ofresource load times 1610 (e.g., Image1, Script1, XHR1, Image2, XHR2, andthen eventually, Script2). Since there is a large gap in the timeline(that is, there is a period of time 1620 with no load times or any othermonitored activity occurring, where that period of time is longer thanthe threshold length of time, e.g., for 5 seconds), notably between XHR2and Script2, the techniques herein classify that time span 1620 asnetwork inactive time. Accordingly, the techniques herein dictate thatthe actual load time 1630 is the time when XHR2 (the max load timestamptime) has loaded on the page prior to reaching that network inactivetime 1620.

Notably, the technique herein have generally used the term “load time”.Those skilled in the art will appreciate that a load time may indicateeither the time of day, or the time since a timer that is initiated (0.0s) at the start time. In either event, either the start time may besubtracted from the end to determine the load time (i.e., calculatingthe length of time required to load the page), or else the load timedirectly implies the length of time required to load the page.

In accordance with the techniques herein, if the load time of a page ishigher than normal (e.g., set baseline or a determined average), thenthat particular page load may be determined to be slower than normal,which can be due to any of the following reasons, among others:

-   -   1. Loading heavy resources on page;    -   2. XHRs are taking more time than usual; and    -   3. Resources are unreachable or otherwise stalled.        For example, when measuring load time of a page in SPA in the        manner above, an accurate measurement of how SPAs are        performing. For example, in a page, if there are a lot of heavy        resources, lots of XHRs, or otherwise, the page will load slowly        and hence effect the load time of that page. As such, measuring        load time of a page is the basic and necessary metric to measure        performance of SPAs, to determine how quickly/slowly the SPA web        application is. Additionally, by calculating the “endTime” of a        page in SPA web application in this manner (i.e., the time at        which page loaded successfully with all resources and XHRs),        then if the loadTime (endTime−startTime) is higher than usual,        it may also mean that the page is designed poorly and should be        optimized. It could also be an indication that the page is using        heavy resources and XHRs, which could be hindering the user        experience.

Notably, the techniques herein can be used for initial page loads andsubsequent new pages (i.e., virtual pages) in SPAs. That is, the samealgorithm described above runs for the initial page load and then againfor any subsequent virtual pages that are loaded. In the event that thata user navigates away from the page in between the start time and theexpected load/end time, the page load time computation algorithm abovemay be stopped before it is able to complete (i.e., when the usernavigates away from the page), and that stop time (e.g., stopped at Xseconds) becomes the page load time (X seconds) for metric tracking oflength of time since the start of the page load to the navigation.

FIG. 17 illustrates an example simplified procedure for determiningload/end times for SPAs in a network in accordance with one or moreembodiments described herein. For example, a non-generic, specificallyconfigured device (e.g., device 200) may perform procedure 1700 byexecuting stored instructions (e.g., Single Page App Monitoring process248). The procedure 1700 may start at step 1705, and continues to step1710, where, as described in greater detail above, a monitoring processdetects a page load start time of an SPA page (e.g., an initial pageload or a subsequent virtual page load) having added direct resourcesand dynamic resources. As mentioned above, the dynamic resources maycomprise XHRs, while the direct resources may comprise images, scripts,stylesheets, fonts, etc.

In step 1715, the monitoring process tracks the direct resources anddynamic resources, and notes a load end time for each of the trackeddirect resources and dynamic resources in step 1720. In particular, asdetailed above, in certain embodiments tracking direct resourcescomprises adding a hook to each added direct resource of the SPA page,adding a load listener to each of the added direct resources, anddetermining the load end time of each load listener when invoked,wherein the load end time for each of the direct resources is the loadend time of a corresponding load listener. Conversely, tracking dynamicresources may comprise adding a hook to each added dynamic resource ofthe SPA page, tracking firing and loading of the added dynamicresources, and determining the load end time of each dynamic resourcewhen loaded. (Note that in either case, adding the hook may comprise aJSAgent in a DOM.)

Resources are tracked until step 1725, where the monitoring processstops the tracking in response to a determination of a thresholdduration of network inactivity (e.g., determining network inactivity inresponse to expiration of the threshold duration since any resourceshave loaded on the SPA page.) At this time, in step 1730, the monitoringprocess may determine a maximum load end time of the tracked directresources and dynamic resources, and can then set a page load time ofthe SPA page as a difference between the maximum load end time and thepage load start time in step 1735. Note that while in one embodiment thestart and end times may be actual times (e.g., 12:34.56 AM), thusrequiring an actual subtraction to determine the load time, in otherembodiments, the page load start time is the initiating time (e.g., is atime set to zero), and load times indicate a length of time since thepage load start time.

Various actions may be performed based on this measured time, such asdetecting and mitigating an anomaly based on the page load time in step1740 as shown, among other possibilities. The simplified procedure 1700may illustratively end in step 1745, until a new page is loaded.

It should be noted that while certain steps within procedure 1700 may beoptional as described above, the steps shown in FIG. 17 are merelyexamples for illustration, and certain other steps may be included orexcluded as desired. Further, while a particular order of the steps isshown, this ordering is merely illustrative, and any suitablearrangement of the steps may be utilized without departing from thescope of the embodiments herein. Moreover, while procedures 1200 and1700 are described separately, certain steps from each procedure may beincorporated into each other procedure, and the procedures are not meantto be mutually exclusive.

The techniques described herein, therefore, also determine end times andload for SPAs, particularly by tracking both direct and dynamicresources (e.g., XHRs) to calculate the “actual load time” with a highdegree of accuracy on all types of web browsers. Notably, markingnetwork inactivity as a time span where nothing is happening (noresources or XHRs are in progress) is performant. Previous solutionshave used different ways to determine network inactivity, but they areless performant and less accurate, and specifically do not account forXHRs fired on the page.

In still further embodiments of the techniques herein, a business impactof end times for SPAs can also be quantified. That is, because of issuesrelated to specific applications/processes (e.g., lost traffic, slowerservers, overloaded network links, etc.), various corresponding businesstransactions may have been correspondingly affected for thoseapplications/processes (e.g., online purchases were delayed, page visitswere halted before fully loading, user satisfaction or dwell timedecreased, etc.), while other processes (e.g., on other network segmentsor at other times) remain unaffected. The techniques herein, therefore,can correlate the end times for SPAs to their network metrics and withvarious business transactions in order to better understand the affectthe end times for SPAs may have had on the business transactions,accordingly.

Illustratively, the techniques described herein may be performed byhardware, software, and/or firmware, such as in accordance with theillustrative Single Page App Monitoring process 248, which may includecomputer executable instructions executed by the processor 220 toperform functions relating to the techniques described herein, e.g., inconjunction with corresponding processes of other devices in thecomputer network as described herein (e.g., on network agents,controllers, computing devices, servers, etc.).

While there have been shown and described illustrative embodimentsabove, it is to be understood that various other adaptations andmodifications may be made within the scope of the embodiments herein.For example, while certain embodiments are described herein with respectto certain types of networks in particular, the techniques are notlimited as such and may be used with any computer network, generally, inother embodiments. Moreover, while specific technologies, protocols, andassociated devices have been shown, such as Java, TCP, IP, and so on,other suitable technologies, protocols, and associated devices may beused in accordance with the techniques described above. In addition,while certain devices are shown, and with certain functionality beingperformed on certain devices, other suitable devices and processlocations may be used, accordingly. That is, the embodiments have beenshown and described herein with relation to specific networkconfigurations (orientations, topologies, protocols, terminology,processing locations, etc.). However, the embodiments in their broadersense are not as limited, and may, in fact, be used with other types ofnetworks, protocols, and configurations.

Moreover, while the present disclosure contains many other specifics,these should not be construed as limitations on the scope of anyinvention or of what may be claimed, but rather as descriptions offeatures that may be specific to particular embodiments of particularinventions. Certain features that are described in this document in thecontext of separate embodiments can also be implemented in combinationin a single embodiment. Conversely, various features that are describedin the context of a single embodiment can also be implemented inmultiple embodiments separately or in any suitable sub-combination.Further, although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

For instance, while certain aspects of the present disclosure aredescribed in terms of being performed “by a server” or “by acontroller”, those skilled in the art will appreciate that agents of theapplication intelligence platform (e.g., application agents, networkagents, language agents, etc.) may be considered to be extensions of theserver (or controller) operation, and as such, any process stepperformed “by a server” need not be limited to local processing on aspecific server device, unless otherwise specifically noted as such.Furthermore, while certain aspects are described as being performed “byan agent” or by particular types of agents (e.g., application agents,network agents, etc.), the techniques may be generally applied to anysuitable software/hardware configuration (libraries, modules, etc.) aspart of an apparatus or otherwise.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. Moreover, the separation of various system components in theembodiments described in the present disclosure should not be understoodas requiring such separation in all embodiments.

The foregoing description has been directed to specific embodiments. Itwill be apparent, however, that other variations and modifications maybe made to the described embodiments, with the attainment of some or allof their advantages. For instance, it is expressly contemplated that thecomponents and/or elements described herein can be implemented assoftware being stored on a tangible (non-transitory) computer-readablemedium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructionsexecuting on a computer, hardware, firmware, or a combination thereof.Accordingly, this description is to be taken only by way of example andnot to otherwise limit the scope of the embodiments herein. Therefore,it is the object of the appended claims to cover all such variations andmodifications as come within the true spirit and scope of theembodiments herein.

1. A method, comprising: detecting, by a monitoring process, a page loadstart time of a single page application (SPA) page having added directresources and dynamic resources; tracking, by the monitoring process,the direct resources and dynamic resources; noting, by the monitoringprocess, a load end time for each of the tracked direct resources anddynamic resources; determining, by the monitoring process, networkinactivity in response to expiration of a threshold duration since anyresources, including the direct resources and the dynamic resources,have loaded on the SPA page; stopping, by the monitoring process and inresponse to determining the network inactivity, the tracking of thedirect resources and dynamic resources; determining, by the monitoringprocess, a maximum load end time of the tracked direct resources anddynamic resources; and setting, by the monitoring process, a page loadtime of the SPA page as a difference between the maximum load end timeand the page load start time.
 2. (canceled)
 3. The method as in claim 1,wherein the dynamic resources comprise Extensible Markup LanguageHypertext Transfer Protocol Requests (XHRs).
 4. The method as in claim1, wherein the direct resources are selected from a group consisting of:images; scripts; stylesheets; and fonts.
 5. The method as in claim 1,wherein tracking direct resources comprises: adding a hook to each addeddirect resource of the SPA page; adding a load listener to each of theadded direct resources; and determining the load end time of each loadlistener when invoked, wherein the load end time for each of the directresources is the load end time of a corresponding load listener.
 6. Themethod as in claim 5, wherein adding the hook comprises a javascriptagent (JSAgent) in a Document Object Model (DOM).
 7. The method as inclaim 1, wherein tracking dynamic resources comprises: adding a hook toeach added dynamic resource of the SPA page; tracking firing and loadingof the added dynamic resources; and determining the load end time ofeach dynamic resource when loaded.
 8. The method as in claim 7, whereinadding the hook comprises a javascript agent (JSAgent) in a DocumentObject Model (DOM).
 9. The method as in claim 1, wherein the page loadstart time is set to zero, and wherein load times indicate a length oftime since the page load start time.
 10. The method as in claim 1,wherein the SPA page is one of either an initial page load or asubsequent virtual page load.
 11. The method as in claim 1, furthercomprising: detecting an anomaly based on the page load time; andmitigating the anomaly.
 12. A tangible, non-transitory,computer-readable medium storing program instructions that cause acomputer to execute a process comprising: detecting a page load starttime of a single page application (SPA) page having added directresources and dynamic resources; tracking the direct resources anddynamic resources; noting a load end time for each of the tracked directresources and dynamic resources; determining network inactivity inresponse to expiration of a threshold duration since any resources,including the direct resources and the dynamic resources, have loaded onthe SPA page; stopping, in response to determining the networkinactivity, the tracking of the direct resources and dynamic resources;determining a maximum load end time of the tracked direct resources anddynamic resources; and setting a page load time of the SPA page as adifference between the maximum load end time and the page load starttime.
 13. (canceled)
 14. The computer-readable medium as in claim 12,wherein the dynamic resources comprise Extensible Markup LanguageHypertext Transfer Protocol Requests (XHRs).
 15. The computer-readablemedium as in claim 12, wherein the direct resources are selected from agroup consisting of: images; scripts; stylesheets; and fonts.
 16. Thecomputer-readable medium as in claim 12, wherein the process, fortracking direct resources, further comprises: adding a hook to eachadded direct resource of the SPA page; adding a load listener to each ofthe added direct resources; and determining the load end time of eachload listener when invoked, wherein the load end time for each of thedirect resources is the load end time of a corresponding load listener.17. The computer-readable medium as in claim 12, wherein the process,for tracking dynamic resources, further comprises: adding a hook to eachadded dynamic resource of the SPA page; tracking firing and loading ofthe added dynamic resources; and determining the load end time of eachdynamic resource when loaded.
 18. The computer-readable medium as inclaim 12, wherein the SPA page is one of either an initial page load ora subsequent virtual page load.
 19. The computer-readable medium as inclaim 12, wherein the process further comprises: detecting an anomalybased on the page load time; and mitigating the anomaly.
 20. Anapparatus, comprising: one or more network interfaces to communicatewith a network; a processor coupled to the network interfaces andconfigured to execute one or more processes; and a memory configured tostore a process executable by the processor, the process when executedconfigured to: detect a page load start time of a single pageapplication (SPA) page having added direct resources and dynamicresources; track the direct resources and dynamic resources; note a loadend time for each of the tracked direct resources and dynamic resources;determine network inactivity in response to expiration of a thresholdduration since any resources, including the direct resources and thedynamic resources, have loaded on the SPA page; stop, in response to adetermination of the network inactivity, the tracking of the directresources and dynamic resources; determine a maximum load end time ofthe tracked direct resources and dynamic resources; and set a page loadtime of the SPA page as a difference between the maximum load end timeand the page load start time.